Patrick Koch

Stefano Gasperini, Patrick Koch, Vinzenz Dallabetta et al.

While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue. In this paper, we present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework. In particular, we show how radar can be used during training as weak supervision signal, as well as an extra input to enhance the estimation robustness at inference time. Since automotive radars are readily available, this allows to collect training data from a variety of existing vehicles. Moreover, by filtering and expanding the signal to make it compatible with learning-based approaches, we address radar inherent issues, such as noise and sparsity. With R4Dyn we are able to overcome a major limitation of self-supervised depth estimation, i.e. the prediction of traffic participants. We substantially improve the estimation on dynamic objects, such as cars by 37% on the challenging nuScenes dataset, hence demonstrating that radar is a valuable additional sensor for monocular depth estimation in autonomous vehicles.

Steven Gardner, Oleg Golovidov, Joshua Griffin et al.

Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits are great. In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems. Autotune is built on a suite of derivative-free optimization methods, and utilizes multi-level parallelism in a distributed computing environment for automatically training, scoring, and selecting good models. Incorporation of multiple objectives and constraints in the model exploration and selection process provides the flexibility needed to satisfy trade-offs necessary in practical machine learning applications. Experimental results from standard multi-objective optimization benchmark problems show that Autotune is very efficient in capturing Pareto fronts. These benchmark results also show how adding constraints can guide the search to more promising regions of the solution space, ultimately producing more desirable Pareto fronts. Results from two real-world case studies demonstrate the effectiveness of the constrained multi-objective optimization capability offered by Autotune.

Patrick Koch, Birgit Hofer, Franz Wotawa

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are more likely to be the root source of bugs. Unfortunately, current spreadsheet smell detection techniques suffer from a number of drawbacks that lead to incorrect or redundant smell reports. For example, the same quality issue is often reported for every copy of a cell, which may overwhelm users. To deal with these issues, we propose to refine spreadsheet smells by exploiting inferred structural information for smell detection. We therefore first provide a detailed description of our static analysis approach to infer clusters and blocks of related cells. We then elaborate on how to improve existing smells by providing three example refinements of existing smells that incorporate information about cell groups and computation blocks. Furthermore, we propose three novel smell detection techniques that make use of the inferred spreadsheet structures. Empirical evaluation of the proposed techniques suggests that the refinements successfully reduce the number of incorrectly and redundantly reported smells, and novel deficits are revealed by the newly introduced smells.

Patrick Koch

Spreadsheets are the go-to tool for computerized calculation and modelling, but are hard to comprehend and adapt after reaching a certain complexity. In general, cognition of complex systems is facilitated by having a higher order mental model of the system in question to work with. We therefore present a concept for structure-aware understanding of and interaction with spreadsheets that extends previous work on structure inference in the domain. Following this concept, structural information is used to enrich visualizations, reactively enhance traditional user actions, and provide tools to proactively alter the overall spreadsheet makeup instead of individual cells The intended systems should, in first approximation, not replace common spreadsheet tools, but provide an additional layer of functionality alongside the established interface. In ongoing work, we therefore implemented a tool for structure inference and visualization along the common spreadsheet layout. Based on this framework, we plan to introduce the envisioned proactive and reactive interaction mechanics, and finally provide structure-aware unctionality as an add-in for common spreadsheet processors. We believe that providing the tools for thinking about and interacting with spreadsheets in this manner will benefit users both in terms of productivity and overall spreadsheet quality.

Patrick Koch, Konstantin Schekotihin, Dietmar Jannach et al.

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.

Patrick Koch, Oleg Golovidov, Steven Gardner et al.

Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quality. For hyperparameter tuning, machine learning algorithms are complex black-boxes. This creates a class of challenging optimization problems, whose objective functions tend to be nonsmooth, discontinuous, unpredictably varying in computational expense, and include continuous, categorical, and/or integer variables. Further, function evaluations can fail for a variety of reasons including numerical difficulties or hardware failures. Additionally, not all hyperparameter value combinations are compatible, which creates so called hidden constraints. Robust and efficient optimization algorithms are needed for hyperparameter tuning. In this paper we present an automated parallel derivative-free optimization framework called \textbf{Autotune}, which combines a number of specialized sampling and search methods that are very effective in tuning machine learning models despite these challenges. Autotune provides significantly improved models over using default hyperparameter settings with minimal user interaction on real-world applications. Given the inherent expense of training numerous candidate models, we demonstrate the effectiveness of Autotune's search methods and the efficient distributed and parallel paradigms for training and tuning models, and also discuss the resource trade-offs associated with the ability to both distribute the training process and parallelize the tuning process.